In an existing signal detection algorithm using a cyclo-stationary characteristic value, a threshold value used for determining whether a signal exists is obtained by applying a temporal average with respect to a summation of cyclo-stationary characteristic values of collected signals. Through the temporal average, the signal detection algorithm can detect a signal by reducing the variance of cyclo-stationary characteristics generated from noise, maintaining cyclo-stationary characteristics of the signals, and relatively emphasizing the cyclo-stationary characteristics in comparison to the noise characteristic.
However, in the case of the signal detection algorithm, when the cyclo-stationary characteristic of the signal is relatively small due to a channel environment and the like, there is a very small difference between a threshold value and a peak value indicating signal characteristics, despite using signals that have been collected for a long period of time. Therefore, it may be difficult to determine whether a signal exists. When an error exists in the threshold value, it may have significant impact on the detection performance. Also, the signal detection algorithm uses a frequency domain signal converted by a fast Fourier transform (FFT). As a number of fast Fourier transformed points increases, a calculation amount may increase.
Although the signal detection scheme using the cyclo-stationary characteristic value has the excellent performance, the above-described disadvantages may constrain the actual usage of the signal detection scheme. Therefore, there is a need for a method that can maintain advantages of the signal detection scheme using the cyclo-stationary characteristic and also can overcome the above-described disadvantages.